Anomaly Detection of Distribution Network Synchronous Measurement Data Based on Large Dimensional Random Matrix

2019 ◽  
pp. 449-458
Author(s):  
Zhongming Chen ◽  
Yaoyu Zhang ◽  
Chuan Qing ◽  
Jierong Liu ◽  
Jiaqi Tang ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Random Matrix ◽  
Distribution Network ◽  
Measurement Data ◽  
Synchronous Measurement ◽  
Large Dimensional Random Matrix

Dynamic simulation of wind field in three-dimensional display space considering particle swarm algorithm fuzzy logic

2021 ◽  
pp. 1-6
Author(s):  
Honglei Xu ◽  
Linhuan Wang
Keyword(s):  
Fuzzy Logic ◽  
Dynamic Simulation ◽  
Wind Field ◽  
Three Dimensional ◽  
Measurement Data ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Three Dimensional Display ◽  
Swarm Algorithm ◽  
Synchronous Measurement

In order to improve the accuracy of dynamic detection of wind field in the three-dimensional display space, system software is carried out on the actual scene and corresponding airborne radar observation information data, and the particle swarm algorithm fuzzy logic algorithm is introduced into the wind field dynamic simulation process in three-dimensional display space, to analyze the error of the filtering result in detail, to process the hurricane Lily Doppler radar measurement data with the optimal adaptive filtering according to the error data. The three-dimensional wind field synchronous measurement data obtained by filtering was compared with three-dimensional wind field synchronous measurement data of the GPS dropsonde in this experiment, the sea surface wind field measurement data of the multi-band microwave radiometer, and the wind field data at aircraft altitude.

scholarly journals Multi Objective for PMU Placement in Compressed Distribution Network Considering Cost and Accuracy of State Estimation

2019 ◽  
Vol 9 (7) ◽  
pp. 1515 ◽  
Author(s):  
Kong ◽  
Wang ◽  
Yuan ◽  
Yu
Keyword(s):  
State Estimation ◽  
Estimation Error ◽  
Distribution Network ◽  
Measurement Data ◽  
Optimal Solution ◽  
Pareto Set ◽  
Measurement Unit ◽  
Nsga Ii ◽  
Multi Objective ◽  
Pmu Placement

A phasor measurement unit (PMU) can provide phasor measurements to the distribution network to improve observability. Based on pre-configuration and existing measurements, a network compression method is proposed to reduce PMU candidate locations. Taking the minimum number of PMUs and the lowest state estimation error as the objective functions and taking full observability of distribution network as the constraint, a multi objective model of optimal PMU placement (OPP) is proposed. A hybrid state estimator based on supervisory control and data acquisition (SCADA) and PMU measurements is proposed. To reduce the number of PMUs required for full observability, SCADA measurement data are also considered into the constraint by update and equivalent. In addition, a non-dominated sorting genetic algorithm-II (NSGA-II) is applied to solve the model to get the Pareto set. Finally, the optimal solution is selected from the Pareto set by the technique for order preference by similarity to ideal solution (TOPSIS). The effectiveness of the proposed method is verified by IEEE standard bus systems.

scholarly journals An Association Rules-Based Method for Outliers Cleaning of Measurement Data in the Distribution Network

2021 ◽  
Vol 9 ◽  
Author(s):  
Hua Kuang ◽  
Risheng Qin ◽  
Mi He ◽  
Xin He ◽  
Ruimin Duan ◽  
...  
Keyword(s):  
Cost Function ◽  
Association Rules ◽  
Mahalanobis Distance ◽  
Spatial Clustering ◽  
Distribution Network ◽  
Measurement Data ◽  
Operation Management ◽  
Test Results ◽  
Cleaning Method ◽  
Outliers Detection

For any power system, the reliability of measurement data is essential in operation, management and also in planning. However, it is inevitable that the measurement data are prone to outliers, which may impact the results of data-based applications. In order to improve the data quality, the outliers cleaning method for measurement data in the distribution network is studied in this paper. The method is based on a set of association rules (AR) that are automatically generated form historical measurement data. First, the association rules are mining in conjunction with the density-based spatial clustering of application with noise (DBSCAN), k-means and Apriori technique to detect outliers. Then, for the outliers repairing process after outliers detection, the proposed method uses a distance-based model to calculate the repairing cost of outliers, which describes the similarity between outlier and normal data. Besides, the Mahalanobis distance is employed in the repairing cost function to reduce the errors, which could implement precise outliers cleaning of measurement data in the distribution network. The test results for the simulated datasets with artificial errors verify that the superiority of the proposed outliers cleaning method for outliers detection and repairing.

Dimension Increased Random Matrix Method for Anomaly Detection in Wireless Networks

2021 ◽  
Author(s):  
Tengfei Sui ◽  
Xiaofeng Tao ◽  
Huici Wu ◽  
Xuefei Zhang ◽  
Jin Xu
Keyword(s):  
Wireless Networks ◽  
Anomaly Detection ◽  
Random Matrix ◽  
Matrix Method

scholarly journals Method for Real-Time Abnormal State Detection of a Distribution Network Based on Maximum and Minimum Eigenvalues

2017 ◽  
Vol 2017 ◽  
pp. 1-8
Author(s):  
Keyan Liu ◽  
Kaiyuan He ◽  
Huanna Niu ◽  
Yuzhu Wang ◽  
Jingxiang Zhao
Keyword(s):  
Real Time ◽  
Physical Model ◽  
Random Matrix ◽  
Distribution Network ◽  
Sudden Change ◽  
Fault Type ◽  
Abnormal State ◽  
Sample Covariance ◽  
State Detection ◽  
Network Operation

The state analysis method of a traditional distribution network operation is strictly dependent on the physical model of itself, but it varies as the geography changes, and it is difficult to find the abnormal state of a district network on real-time, especially the sudden change caused by the distributed energy and EV load. So, a method of the abnormal state detecting for the distribution network is proposed based on the maximum and minimum eigenvalues. Firstly, a high-dimensional random matrix is established by the big data from the distribution network management system to take abnormal state detection through a real-time sliding window. Then, the maximum and minimum eigenvalues of the distribution network are gained by calculating the sample covariance matrix of the random matrix and determining the maximum and minimum eigenvalues of the latter matrix. Finally, an 1177-node testing system was taken as an example, and the simulation results showed that the proposed method could detect the abnormal state in real-time without depending on the physical model and fault type of the grid.

scholarly journals A Fast Reactive Power Optimization in Distribution Network Based on Large Random Matrix Theory and Data Analysis

2016 ◽  
Vol 6 (6) ◽  
pp. 158 ◽  
Author(s):  
Wanxing Sheng ◽  
Keyan Liu ◽  
Hongyan Pei ◽  
Yunhua Li ◽  
Dongli Jia ◽  
...  
Keyword(s):  
Data Analysis ◽  
Random Matrix Theory ◽  
Random Matrix ◽  
Reactive Power ◽  
Power Optimization ◽  
Matrix Theory ◽  
Distribution Network ◽  
Reactive Power Optimization
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